4.1.1 Visual exploration - Univariate Analysis

recipes %>% 
  ggplot(aes(x=as.factor(rating), fill=as.factor(rating) )) +  
  geom_bar( ) +
  scale_fill_manual(values = c("red4", "red3", "orangered", "orange", "gold", "greenyellow", "green3", "green4") ) +
  theme(legend.position="none") +
  scale_y_continuous(breaks=seq(0,10000,1000)) +
  labs(x = "Rating", y = "Number of recipes", 
       title = "Overview of recipes' ratings")

recipes_plot <- recipes %>% 
  pivot_longer(cols = c(calories, protein, fat, sodium),
               names_to = "nutrition",
               values_to = "n_value")

# Calories boxplot not filtered
recipes_plot %>%
  filter(nutrition == "calories") %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme_light() +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  ggtitle("Boxplot of calories nutritional value") +
  xlab("") +
  ylab("Value")

recipes_plot %>% 
  filter(nutrition == "calories") %>% 
  select(title, nutrition, n_value) %>% 
  arrange(desc(n_value)) 

#> # A tibble: 10,321 x 3
#>    title                                            nutrition n_value
#>    <chr>                                            <chr>       <dbl>
#>  1 "Pear-Cranberry Mincemeat Lattice Pie "          calories   3.01e7
#>  2 "Deep-Dish Wild Blueberry Pie "                  calories   3.00e7
#>  3 "Apricot, Cranberry and Walnut Pie "             calories   1.31e7
#>  4 "Lamb Köfte with Tarator Sauce "                 calories   4.52e6
#>  5 "Rice Pilaf with Lamb, Carrots, and Raisins "    calories   4.16e6
#>  6 "Chocolate-Almond Pie "                          calories   3.36e6
#>  7 "Caramelized Apple and Pear Pie "                calories   3.36e6
#>  8 "Merguez Lamb Patties with Golden Raisin Cousco~ calories   5.45e4
#>  9 "Grilled Lamb Chops with Porcini Mustard "       calories   2.41e4
#> 10 "Braised Short Ribs with Red Wine Gravy "        calories   1.96e4
#> # i 10,311 more rows

# Calories boxplot
df <- recipes_plot %>%
  filter(nutrition == "calories", n_value <= 7000)

boxplot_calories <- df %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
  geom_boxplot()+
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  theme(legend.position="none",
        plot.title = element_text(size=11)) +
  scale_y_continuous(breaks=seq(0,7000,500)) +
  labs(x = "", y = "Calories", title = "Boxplot of calories nutritional value filtered")


# Calories histogram
histogram_calories <- df %>% 
  ggplot(aes(x=n_value)) +
  geom_histogram(binwidth=50, fill="red3", color="red3", alpha=0.9) +
  theme(plot.title = element_text(size=11)) +
  scale_x_continuous(breaks=seq(0,10000,1000)) +
  scale_y_continuous(breaks=seq(0,1750,250)) +
  labs(x = "Count", y = "Calories", title = "Distribution of calories across all recipes")


# Calories density plot
density_calories <- df %>% 
  ggplot(aes(x=n_value)) +
  geom_density(fill="red3", color="red2", alpha=0.8) +
  theme(plot.title = element_text(size=11)) +
  scale_x_continuous(breaks=seq(0,10000,1000)) +
  ggtitle("Distribution of calories across all recipes") +
  xlab("Calories")

grid.arrange(boxplot_calories, histogram_calories, density_calories, ncol=2, nrow =2)

recipes_plot %>%
  filter(nutrition == "protein") %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme_light() +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  ggtitle("Boxplot of protein nutritional value") +
  xlab("") +
  ylab("Value")

recipes_plot %>%
  filter(nutrition == "protein") %>% 
  select(title, nutrition, n_value) %>% 
  arrange(desc(n_value)) 

#> # A tibble: 10,321 x 3
#>    title                                            nutrition n_value
#>    <chr>                                            <chr>       <dbl>
#>  1 "Rice Pilaf with Lamb, Carrots, and Raisins "    protein    236489
#>  2 "Pear-Cranberry Mincemeat Lattice Pie "          protein    200968
#>  3 "Deep-Dish Wild Blueberry Pie "                  protein    200210
#>  4 "Lamb Köfte with Tarator Sauce "                 protein    166471
#>  5 "Apricot, Cranberry and Walnut Pie "             protein     87188
#>  6 "Chocolate-Almond Pie "                          protein     58334
#>  7 "Caramelized Apple and Pear Pie "                protein     58324
#>  8 "Merguez Lamb Patties with Golden Raisin Cousco~ protein      2074
#>  9 "Manhattan Clam Chowder "                        protein      1625
#> 10 "Clam and Oyster Chowder "                       protein      1365
#> # i 10,311 more rows

# Proteins boxplot
boxplot_protein <- recipes_plot %>%
  filter(nutrition == "protein", n_value <= 1000) %>% 
  ggplot( aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  scale_y_continuous(breaks=seq(0,7000,25)) +
  ggtitle("Boxplot of protein nutritional value filtered") +
  xlab("") +
  ylab("Proteins") 


# Proteins histogram
histogram_protein <- recipes_plot %>% 
  filter(nutrition == "protein", n_value <= 1000) %>% 
  ggplot(aes(x=n_value)) +
  geom_histogram(binwidth=7, fill="red3", color="red3", alpha=0.9) +
  theme(plot.title = element_text(size=15)) +
  scale_x_continuous(breaks=seq(0,1000,50)) +
  scale_y_continuous(breaks=seq(0,7000,250)) +
  ggtitle("Distribution of proteins across all recipes") +
  xlab("Proteins") +
  ylab("Count") 


# Protein density plot
density_protein <- recipes_plot %>% 
  filter(nutrition == "protein", n_value <= 1000) %>% 
  ggplot(aes(x=n_value)) +
  geom_density(fill="red3", color="red2", alpha=0.8) +
  scale_x_continuous(breaks=seq(0,1000,50)) +
  ggtitle("Distribution of proteins across all recipes") +
  xlab("Proteins")

grid.arrange(boxplot_protein, histogram_protein, density_protein, ncol=2, nrow =3)

recipes_plot %>%
  filter(nutrition == "sodium") %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  ggtitle("Boxplot of sodium nutritional value") +
  xlab("") +
  ylab("Value")

recipes_plot %>% 
  filter(nutrition == "sodium") %>%
  select(title, nutrition, n_value) %>% 
  arrange(desc(n_value)) 

#> # A tibble: 10,321 x 3
#>    title                                           nutrition  n_value
#>    <chr>                                           <chr>        <dbl>
#>  1 "Pear-Cranberry Mincemeat Lattice Pie "         sodium    27675110
#>  2 "Deep-Dish Wild Blueberry Pie "                 sodium    27570999
#>  3 "Apricot, Cranberry and Walnut Pie "            sodium    12005810
#>  4 "Lamb Köfte with Tarator Sauce "                sodium     7540990
#>  5 "Chocolate-Almond Pie "                         sodium     3449512
#>  6 "Caramelized Apple and Pear Pie "               sodium     3449373
#>  7 "Rice Pilaf with Lamb, Carrots, and Raisins "   sodium     3134853
#>  8 "Whole Branzino Roasted in Salt "               sodium      132220
#>  9 "Red Snapper Baked in Salt with Romesco Sauce " sodium      132025
#> 10 "Scallops with Mushrooms in White-Wine Sauce "  sodium       90572
#> # i 10,311 more rows

# Sodium boxplot
boxplot_sodium <- recipes_plot %>%
  filter(nutrition == "sodium", n_value <= 30000) %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  scale_y_continuous(breaks=seq(0,30000,500)) +
  ggtitle("Boxplot of sodium nutritional value") +
  xlab("") +
  ylab("Sodium") 


# Sodium histogram
histogram_sodium <- recipes_plot %>%
  filter(nutrition == "sodium", n_value <= 30000) %>% 
  ggplot(aes(x=n_value)) +
  geom_histogram(binwidth=50, fill="red3", color="red3", alpha=0.9) +
  theme(plot.title = element_text(size=15)) +
  scale_x_continuous(breaks=seq(0,30000,1000)) +
  scale_y_continuous(breaks=seq(0,1750,250)) +
  ggtitle("Distribution of sodium across all recipes") +
  xlab("Sodium") +
  ylab("Count") 


# Sodium density plot
density_sodium <- recipes_plot %>%
  filter(nutrition == "sodium", n_value <= 30000) %>% 
  ggplot(aes(x=n_value)) +
  geom_density(fill="red3", color="red2", alpha=0.8) +
  scale_x_continuous(breaks=seq(0,30000,1000)) +
  ggtitle("Distribution of sodium across all recipes") +
  xlab("Sodium") 


grid.arrange(boxplot_sodium, histogram_sodium, density_sodium, ncol=1, nrow =3)

recipes_plot %>%
  filter(nutrition == "fat") %>% 
  ggplot(aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme_light() +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  ggtitle("Boxplot of fat nutritional value") +
  xlab("") +
  ylab("Value")

recipes_plot %>% 
  filter(nutrition == "fat") %>% 
  select(title, nutrition, n_value) %>% 
  arrange(desc(n_value)) 

#> # A tibble: 10,321 x 3
#>    title                                           nutrition n_value
#>    <chr>                                           <chr>       <dbl>
#>  1 "Pear-Cranberry Mincemeat Lattice Pie "         fat       1722763
#>  2 "Deep-Dish Wild Blueberry Pie "                 fat       1716279
#>  3 "Apricot, Cranberry and Walnut Pie "            fat        747374
#>  4 "Rice Pilaf with Lamb, Carrots, and Raisins "   fat        221495
#>  5 "Chocolate-Almond Pie "                         fat        186660
#>  6 "Caramelized Apple and Pear Pie "               fat        186642
#>  7 "Lamb Köfte with Tarator Sauce "                fat         44198
#>  8 "Grilled Lamb Chops with Porcini Mustard "      fat          2228
#>  9 "Braised Short Ribs with Red Wine Gravy "       fat          1818
#> 10 "Braised Duck Legs with Shallots and Parsnips " fat          1610
#> # i 10,311 more rows

# Fat boxplot
boxplot_fat <- recipes_plot %>%
  filter(nutrition == "fat", n_value <= 40000) %>% 
  ggplot( aes(x=nutrition, y=n_value, fill=nutrition)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
    theme_light() +
    theme(legend.position="none",
          plot.title = element_text(size=11)) +
  scale_y_continuous(breaks=seq(0,3000,100)) +
  ggtitle("Boxplot of fat nutritional value filtered") +
  xlab("") +
  ylab("Fat") 


# Fat histogram
histogram_fat <- recipes_plot %>% 
  filter(nutrition == "fat", n_value <= 40000) %>% 
  ggplot(aes(x=n_value)) +
  geom_histogram(binwidth=7, fill="red3", color="red3", alpha=0.9) +
  theme(plot.title = element_text(size=15)) +
  ggtitle("Distribution of fat across all recipes") +
  scale_x_continuous(breaks=seq(0,3000,100)) +
  scale_y_continuous(breaks=seq(0,7000,250)) +
  xlab("Fat") +
  ylab("Count") 

# Sodium density plot
density_fat<- recipes_plot %>%
  filter(nutrition == "fat", n_value <= 40000) %>% 
  ggplot(aes(x=n_value)) +
  geom_density(fill="red3", color="red2", alpha=0.8) +
  scale_x_continuous(breaks=seq(0,3000,100)) +
  ggtitle("Distribution of sodium across all recipes") +
  xlab("Fat") 

grid.arrange(boxplot_fat, histogram_fat, density_fat, ncol=3)

4.1.2 Visual exploration - Multivariate Analysis

#removing 41 outliers we discovered above from the recipes df
recipes <- recipes %>% 
  filter(calories <= 7000, protein <= 1000, sodium <= 30000, fat <= 40000)


# Scatterplot of Rating-Calories
sp1 <- recipes %>% 
  ggplot(aes(x=calories, y=rating)) +
  geom_point(alpha=.5) +
  ggtitle("Scatterplot of rating against calories") +
  xlab("Calories") +
  ylab("Rating")

# Scatterplot of Rating-Protein
sp2 <- recipes %>% 
  ggplot(aes(x=protein, y=rating)) +
  geom_point(alpha=.5) +
  ggtitle("Scatterplot of rating against proteins") +
  xlab("Proteins") +
  ylab("Rating") 

# Scatterplot of Rating-Fat
sp3 <- recipes %>% 
  ggplot(aes(x=fat, y=rating)) +
  geom_point(alpha=.5) +
  ggtitle("Scatterplot of rating against fat") +
  xlab("Fat") +
  ylab("Rating") 

# Scatterplot of Rating-Sodium
sp4 <- recipes %>% 
  ggplot(aes(x=sodium, y=rating)) +
  geom_point(alpha=.5) +
  ggtitle("Scatterplot of rating against sodium") +
  xlab("Sodium") +
  ylab("Rating") 

grid.arrange(sp1, sp2, sp3, sp4, ncol=2, nrow =2)

corr_nutritional_values = recipes %>% 
  select(rating, calories, protein, fat, sodium) %>% 
  cor()

corrplot(corr_nutritional_values)

# Grouped scatter of calories and fat
recipes_plot1 <- recipes %>% 
  filter(fat <= 400, calories <= 6000) %>% 
  ggplot(aes(x=calories, y=fat, color=rating)) +
  geom_point(alpha=.5) + 
  scale_color_gradientn(colours = rainbow(5))

# Grouped scatter of calories and protein
recipes_plot2 <- recipes %>% 
  filter(protein <= 500, calories <= 6000) %>% 
  ggplot(aes(x=calories, y=protein, color=rating)) +
  geom_point(alpha=.5) + 
  scale_color_gradientn(colours = rainbow(5))

# Grouped scatter of protein and fat
recipes_plot3 <- recipes %>% 
  filter(fat <= 400, protein <= 350) %>% 
  ggplot(aes(x=protein, y=fat, color=rating)) +
  geom_point(alpha=.5) + 
  scale_color_gradientn(colours = rainbow(5))


# Grouped scatter of protein and sodium
recipes_plot4 <- recipes %>% 
  filter(sodium <= 400, protein <= 350) %>%  
  ggplot(aes(x=protein, y=sodium, color=rating)) +
  geom_point(alpha=.5) + 
  scale_color_gradientn(colours = rainbow(5))

grid.arrange(recipes_plot1, recipes_plot2, recipes_plot3, recipes_plot4, ncol=2, nrow =2)

4.2.1 Feature engineering

We discovered that the variable “drinks” on it’s own had only 11 observations, 4 of which also had the value “drink” equal to 1. Therefore, we decided to merge the two columns to simplify working with a single category called “drink” for all drinks.

#Creating a new dataframe with only the ID, title and the ingredients
ingredients_df <- recipes %>% 
  mutate(drink = ifelse(drink == 1 | drinks == 1, 1, 0)) %>% #merging drinks and drink
  select(ID, title, all_of(all_ingredients), rating)

ingredients_df_bin <- ingredients_df %>%
  mutate(vegetables_bin = as.numeric(if_any(all_of(vegetables_vec), ~.x == 1, na.rm = TRUE)),
         meats_bin = as.numeric(if_any(all_of(all_meats), ~.x == 1, na.rm = TRUE)),
         fish_bin = as.numeric(if_any(all_of(fish_vec), ~.x == 1, na.rm = TRUE)),
         seafood_bin = as.numeric(if_any(all_of(seafood_vec), ~.x == 1, na.rm = TRUE)),
         herbs_bin = as.numeric(if_any(all_of(herbs_vec), ~.x == 1, na.rm = TRUE)),
         nuts_bin = as.numeric(if_any(all_of(nuts_vec), ~.x == 1, na.rm = TRUE)),
         fruits_bin = as.numeric(if_any(all_of(fruits_vec), ~.x == 1, na.rm = TRUE)),
         cheese_bin = as.numeric(if_any(all_of(cheeses_vec), ~.x == 1, na.rm = TRUE)),
         dairy_bin = as.numeric(if_any(all_of(dairy_vec), ~.x == 1, na.rm = TRUE)),
         spices_bin = as.numeric(if_any(all_of(spices_vec), ~.x == 1, na.rm = TRUE)),
         cereals_bin = as.numeric(if_any(all_of(cereals_vec), ~.x == 1, na.rm = TRUE)),
         carbs_bin = as.numeric(if_any(all_of(carbs_vec), ~.x == 1, na.rm = TRUE)),
         dessert_bin = as.numeric(if_any(all_of(dessert_vec), ~.x == 1, na.rm = TRUE)),
         egg_bin = (egg)
         ) %>% 
  select(ID, title, contains("bin"), everything())

####testing if I still need to include the general category to create the binary column now that I modified the df to only include recipes with ingredients specified
# 
# #6586
# ingredients_df %>%
#   mutate(vegetables_bin = as.factor(as.numeric(if_any(c(vegetable, all_of(vegetables_vec)), ~.x == 1, na.rm = TRUE)))) %>%
#   filter(vegetables_bin == 1)
# 
# #6586
# ingredients_df %>%
#   mutate(vegetables_bin = as.factor(as.numeric(if_any(all_of(vegetables_vec), ~.x == 1, na.rm = TRUE)))) %>%
#   filter(vegetables_bin == 1)

ingredients_df_total <- ingredients_df %>%
  mutate(total_ingredients = rowSums(select(., c(all_of(all_ingredients)))),
         total_vegetables = rowSums(select(., c(all_of(vegetables_vec)))),
         total_meat = rowSums(select(., c(all_of(all_meats)))),
         total_fish = rowSums(select(., c(all_of(fish_vec)))),
         total_seafood = rowSums(select(., c(all_of(seafood_vec)))),
         total_herbs = rowSums(select(., c(all_of(herbs_vec)))),
         total_nuts = rowSums(select(., c(all_of(nuts_vec)))),
         total_fruits = rowSums(select(., c(all_of(fruits_vec)))),
         total_cheese = rowSums(select(., c(all_of(cheeses_vec)))),
         total_dairy= rowSums(select(., c(all_of(dairy_vec)))),
         total_spices= rowSums(select(., c(all_of(spices_vec)))),
         total_cereals= rowSums(select(., c(all_of(cereals_vec)))),
         total_carbs = rowSums(select(., c(all_of(carbs_vec)))),
         total_dessert = rowSums(select(., c(all_of(dessert_vec))))
         ) %>% 
  select(ID, title, contains("total"), everything())

total_join <- ingredients_df_total %>% 
  select(ID, contains("total"))
  
ingredients_df_full <- ingredients_df_bin %>% 
  left_join(total_join) %>% 
  select(ID, title, rating, contains("bin"), contains("total"), everything())

4.2.2 “Binary” engineered ingredients categories

#creating a vector with colnames of all the binary columns to be able to select them more easily afterwards
binary_columns <- ingredients_df_bin %>% 
  select(contains("bin")) %>% 
  colnames()

#adding binary columns to ingredients_df
total_categories <- ingredients_df_bin %>% 
  select(ID, all_of(binary_columns)) %>% 
  pivot_longer(-ID, names_to = "category", values_to = "binary_value") %>% 
  group_by(category) %>% 
  summarise(total = sum(binary_value))

#plotting the frequency of binary columns
total_categories %>%
  ggplot(aes(x=reorder(category,total), y=total, fill=total)) +
  geom_bar(stat = "identity") +
  scale_x_discrete(guide = guide_axis(n.dodge=3))+
  scale_fill_viridis() +
  labs(x = "Category", y = "Amount of recipes", title = "Total amount of recipes containing at least one ingredient in defined categories")

ingredients_df_bin %>% 
  select(contains("bin")) %>%
  mutate(across(everything(), as.factor)) %>% 
  plot_bar(order_bar = FALSE)

ingredients_df_bin %>% 
  select(contains("bin"), rating) %>%
  mutate(across(everything(), as.factor)) %>% 
  plot_bar(by = "rating", order_bar = FALSE)

#corr plot
ingredients_df_bin %>% 
  select(contains("bin"), rating) %>%
  plot_correlation()

# # With rating as a factor
# ingredients_df_bin %>% 
#   select(contains("bin"), rating) %>% 
#   mutate(across(rating, as.factor)) %>% 
#   plot_correlation()
# 
# #From this graph we also notice that recipes with rating at 3.75 have slightly positive relationship with carbs and vegetables, whereas recipes with rating at 5 have slightly negative relationship with the same features. We assume then that recipes containing vegetables and carbs tend to be less appreciated.

ingredients_df_bin %>% 
  select(contains("bin"), rating) %>%
  mutate(rating_bin = ifelse(rating>4, "good", "bad"), across(everything(), as.factor)) %>% 
  select(-rating) %>% 
  plot_bar(by = "rating_bin", order_bar = FALSE)

#corr plot
ingredients_df_bin %>% 
  select(contains("bin"), rating) %>%
  mutate(rating_bin = ifelse(rating>4, 1, 0)) %>% 
  select(-rating) %>% 
  plot_correlation()

4.2.3 “Total” engineered ingredients variables

#Analysis which single ingredient is present in most recipes
df <- ingredients_df %>% 
  select(-title, -rating) %>% 
  pivot_longer(-ID, names_to = "ingredient", values_to = "value")


ing_top10 <- df %>% 
  group_by(ingredient) %>% 
  summarise(total = sum(value)) %>% 
  ungroup() %>% 
  arrange(desc(total)) %>% 
  dplyr::slice(1:10)

ing_top10 %>% 
  # mutate(ingredient = fct_rev(ingredient)) %>% 
  ggplot(aes(x=reorder(ingredient, total), y=total, fill=ingredient)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis(discrete = TRUE) +
  scale_x_discrete(guide = guide_axis(n.dodge=2))+
  labs(x = "Ingredient", y = "Value", title = "Total amount of recipes containing each ingredient\nTop 10")

As we can observe, many recipes contain milk cream, onion, tomato, salad, egg and garlic. Some of these are versatile as they can be used for many different kinds of recipes. Onion and garlic are widely used for giving flavour to many dishes, whereas egg and milk cream can be used to cook salty and sweet recipes.

ingredients_df_total %>% 
  select(contains("total"), rating) %>% 
  plot_correlation()

#corr plot
ingredients_df_total %>% 
  select(contains("total"), rating) %>%
  mutate(rating_bin = ifelse(rating>4, 1, 0)) %>% 
  select(-rating) %>% 
  plot_correlation()

#checking some stuff about the new ingredients table
ingredients_df_total %>% 
  select(ID, title, total_ingredients) %>% 
  ggplot(aes(x=total_ingredients)) + 
  geom_bar() + geom_vline(aes(xintercept=mean(total_ingredients)),color="red", linetype="dashed", size=1)+
   scale_x_continuous(breaks = seq(1, 12, by = 1)) +
  labs(x="Number of ingredients per recipe", y = "Recipe Count", title = "Distrubution of number of ingredients per recipe")

#we notice that 117 (no NAs and RAT0, and not duplicated) recipes have 0 ingredients, let's investigate why and how that's possible
ingredients_df_total %>% 
  filter(total_ingredients==0)

#let's pick recipe ID number 1183 which should have poppy and sesame seeds according to the title
recipes %>% 
  filter(ID == 1183) %>% 
  select_if(~ any(. == 1))
#we can see that only 3 variables are equal to 1 here
recipes %>% 
  filter(ID == 365) %>% 
  select_if(~ any(. == 1))

recipes %>% 
  filter(ID == 1089) %>% 
  select_if(~ any(. == 1))

#####
#QUESTION: do we want to keep those recipes?
#####


ingredients_df_total %>% 
  filter(total_ingredients >10)

#eliminating all recipes which do not contain any ingredients that we have classified in categories --> within the "all_ingredients" vector
index_zero_ingredient <- ingredients_df_full %>% 
  filter(total_ingredients == 0) %>% pull(ID)

#removing the 117 recipes with no ingredients listed from recipes and ingredients_df_full
recipes <- recipes %>% 
  filter(!ID %in% index_zero_ingredient)

ingredients_df_full <- ingredients_df_full %>%
  filter(!total_ingredients == 0)

show_nveg <- ingredients_df_full %>%
  filter(vegetables_bin == 1) %>% 
  select(ID, title, total_vegetables) %>% 
  ggplot(aes(x=total_vegetables)) + 
  scale_x_continuous(breaks = seq(1, 9, by = 1)) +
  geom_bar() + geom_vline(aes(xintercept=mean(total_vegetables)),color="blue", linetype="dashed", size=1)


show_nfruit <- ingredients_df_full %>%
  filter(fruits_bin == 1) %>% 
  select(ID, title, total_fruits) %>% 
  ggplot(aes(x=total_fruits)) + 
  scale_x_continuous(breaks = seq(1, 9, by = 1)) +
  geom_bar() + geom_vline(aes(xintercept=mean(total_fruits)),color="blue", linetype="dashed", size=1)


# ingredients_df_full %>% 
#   select(ID, title, total_meat) %>% 
#   ggplot(aes(x=total_meat)) + 
#   geom_bar() + geom_vline(aes(xintercept=mean(total_meat)),color="blue", linetype="dashed", size=1)+
#   labs(x="Number of meats per recipe", y = "Recipe Count", title = "Distrubution of number of meats per recipe")

#let's try to filter by recipes which contain meat to see if my functions work
show_nmeat <- ingredients_df_full %>% 
  filter(meats_bin == 1) %>% 
  select(ID, title, total_meat) %>% 
  ggplot(aes(x=total_meat)) + 
  geom_bar() + geom_vline(aes(xintercept=mean(total_meat)),color="blue", linetype="dashed", size=1)+
  labs(x="Number of meats per recipe", y = "Recipe Count", title = "Distrubution of number of meats per recipe")

#why do we still have value in 0 meats --> it was because when creating the total meat column in ingredients_df_test I did not include the general meat category

grid.arrange(show_nveg, show_nfruit, show_nmeat, ncol=2, nrow =2)

4.3.1 Correlation between nutritional values and engineered ingredients variables

recipes_full %>% 
  select(-ID) %>% 
  plot_correlation()

For instance we notice a positive correlation between proteins and meats_bin which includes all sorts of meat. Another visible positive correlation is the one between sodium and seafood_bin. We might also want to investigate the relationship between calories and carbs_bin.

4.3.2 Barplot and boxplot - Meat and Proteins

# Barplot
barplot1 <- recipes_full %>% 
  ggplot(aes(x = factor(meats_bin), y = protein)) +
  stat_summary(fun = mean, geom = "bar") +
  ggtitle("Average amount of proteins per recipe with and without meat") +
  xlab("Presence of Meat or not") +
  ylab("Protein Content in grams")

# Boxplots per different kinds of meat
recipes_general <- recipes_full %>% 
  select(ID) %>% 
  left_join(recipes, 
           by=c('ID'))

recipes_meat <- recipes_general %>% 
  select(ID, title, rating, calories, protein, fat, sodium, all_of(all_meats))

recipes_meat <- recipes_meat %>% 
  pivot_longer(cols=c("beef", "beef_rib", "beef_shank", "beef_tenderloin", "brisket", "ground_beef", "hamburger", "veal", "bacon", "ham", "pork", "pork_chop", "pork_rib", "pork_tenderloin", "prosciutto", "ground_lamb", "lamb", "lamb_chop", "lamb_shank", "rack_of_lamb", "chicken", "duck", "goose", "poultry", "poultry_sausage", "quail", "turkey", "meatball", "meatloaf", "rabbit", "sausage", "steak", "venison" ),
                    names_to='meats',
                    values_to='yes_or_no') %>% 
  filter(yes_or_no == 1)

multi_boxplot1 <- recipes_meat %>% 
  filter(protein < 450) %>%
  ggplot(aes(x=meats, y=protein, fill=meats)) +
  geom_boxplot(alpha=0.3) +
  scale_y_continuous(breaks=seq(0,7000,25)) +
  coord_flip() +
  ggtitle("Distribution of proteins per recipe according to different kinds of meat") +
  xlab("Meats") +
  ylab("Protein Content in grams") +
  theme(legend.position="none") 

# Here we want to show which kinds of meat specifically have a high level of proteins

grid.arrange(barplot1, multi_boxplot1, ncol=2, nrow =1)

It is very clear that among the recipes with meat, the average content of protein is higher than in recipes without meat. Among those that have meat we notice that goose, venison and lamb shank have the highest content of protein.

4.3.3 Barplot and boxplot - Seafood and Sodium

# Seafood and sodium
barplot2 <- recipes_full %>% 
  ggplot(aes(x = factor(seafood_bin), y = sodium)) +
  stat_summary(fun = mean, geom = "bar") +
  ggtitle("Average amount of sodium per recipe with and without seafood")  +
  xlab("Presence of Seafood or not") +
  ylab("Sodium Content in milligrams")


# Boxplots per different kinds of seafood

recipes_seafood <- recipes_general %>% 
  select(ID, title, rating, calories, protein, fat, sodium, all_of(seafood_vec))

recipes_seafood <- recipes_seafood %>% 
  pivot_longer(cols=c("clam", "crab", "lobster", "mussel", "octopus", "oyster", "scallop", "shellfish", "shrimp", "squid" ),
                    names_to='seafoods',
                    values_to='yes_or_no') %>% 
  filter(yes_or_no == 1)

multi_boxplot2 <- recipes_seafood %>% 
  filter(sodium < 10000) %>%
  ggplot(aes(x=seafoods, y=sodium, fill=seafoods)) +
  geom_boxplot(alpha=0.3) +
  scale_y_continuous(breaks=seq(0,30000,500)) +
  coord_flip() +
  ggtitle("Distribution of sodium per recipe according to different kinds of seafood") +
  xlab("Seafood") +
  ylab("Sodium Content in milligrams") +
  theme(legend.position="none") 

# Here we want to show which kinds of seafood specifically have a high level of sodium

grid.arrange(barplot2, multi_boxplot2, ncol=2, nrow =1)

In this case it is very clear that among the recipes with seafood, the average content of sodium is higher than in recipes without seafood. Among those that have seafood we notice that clams and lobsters have the highest content of sodium.

4.3.4 Barplot and boxplot - Carbs and Calories

# Carbs and calories
barplot3 <- recipes_full %>% 
  ggplot(aes(x = factor(carbs_bin), y = calories)) +
  stat_summary(fun = mean, geom = "bar") +
  ggtitle("Average amount of calories per recipe with and without carbohydrates") +
  xlab("Presence of carbohydrates or not") +
  ylab("Calories content")

# Afterwards we would also want to show which kinds of carbs specifically have a high number of calories


# Boxplots per different kinds of carbs
recipes_carbs <- recipes_general %>% 
  select(ID, title, rating, calories, protein, fat, sodium, all_of(carbs_vec))

recipes_carbs <- recipes_carbs %>% 
  pivot_longer(cols=c("brown_rice", "chickpea", "cornmeal", "couscous", "hominy_cornmeal_masa", "orzo", "pasta", "potato", "rice", "semolina", "sweet_potato_yam", "wild_rice"),
                    names_to='carbs',
                    values_to='yes_or_no') %>% 
  filter(yes_or_no == 1)

multi_boxplot3 <- recipes_carbs %>% 
  filter(sodium < 10000) %>%
  ggplot(aes(x=carbs, y=calories, fill=carbs)) +
  geom_boxplot(alpha=0.3) +
  scale_y_continuous(breaks=seq(0,7000,500)) +
  coord_flip() +
  ggtitle("Distribution of calories per recipe according to different kinds of food high in carbohydrates") +
  xlab("Carbs") +
  ylab("Calories Content") +
  theme(legend.position="none") 

grid.arrange(barplot3, multi_boxplot3, ncol=2, nrow =1)

Recipes which contain carbs register a higher average content of calories than recipes without carbs. Among those that have carbs we notice that pasta and chickpeas are the richest in calories.

4.4.1 Exploratory PCA Analysis

• With only the ones we believe could be useful

nutritional_df <- recipes %>% 
  select(ID, all_of(nutritional_values))

###### CAREFUL --> recipes_analysis should be of dim 10163 x 33
recipes_analysis <- ingredients_df_full %>% 
  left_join(nutritional_df, by="ID") %>% 
  mutate(across(all_of(contains("bin"))), ID = as.character(ID)) %>% 
  select(rating, all_of(nutritional_values), contains("bin"), contains("total"))

recipes_pca2 <- PCA(recipes_analysis, ncp = 33, graph = FALSE)

recipes_pca2

#> **Results for the Principal Component Analysis (PCA)**
#> The analysis was performed on 10163 individuals, described by 33 variables
#> *The results are available in the following objects:
#> 
#>    name               description                          
#> 1  "$eig"             "eigenvalues"                        
#> 2  "$var"             "results for the variables"          
#> 3  "$var$coord"       "coord. for the variables"           
#> 4  "$var$cor"         "correlations variables - dimensions"
#> 5  "$var$cos2"        "cos2 for the variables"             
#> 6  "$var$contrib"     "contributions of the variables"     
#> 7  "$ind"             "results for the individuals"        
#> 8  "$ind$coord"       "coord. for the individuals"         
#> 9  "$ind$cos2"        "cos2 for the individuals"           
#> 10 "$ind$contrib"     "contributions of the individuals"   
#> 11 "$call"            "summary statistics"                 
#> 12 "$call$centre"     "mean of the variables"              
#> 13 "$call$ecart.type" "standard error of the variables"    
#> 14 "$call$row.w"      "weights for the individuals"        
#> 15 "$call$col.w"      "weights for the variables"

fviz_pca_var(recipes_pca2)

fviz_contrib(recipes_pca2, choice = "var", axes = 1)

fviz_contrib(recipes_pca2, choice = "var", axes = 2)

fviz_pca_biplot(recipes_pca2) ## biplot

fviz_eig(recipes_pca2, addlabels = TRUE, ncp=11)

p1 <- fviz_pca_biplot(recipes_pca2, axes = 1:2) 
p2 <- fviz_pca_biplot(recipes_pca2, axes = 3:4) 
p3 <- fviz_pca_biplot(recipes_pca2, axes = 5:6) 
p4 <- fviz_pca_biplot(recipes_pca2, axes = 7:8) 
p5 <- fviz_pca_biplot(recipes_pca2, axes = 9:10) 

grid.arrange(p1, p2, p3, p4, p5, nrow = 3, ncol=2)

recipe_hc2 <- hclust(dist(recipes_analysis, method = "manhattan"))

recipe_clust2 <- cutree(recipe_hc2, k = 10)

fviz_pca_biplot(recipes_pca2,
             col.ind = factor(recipe_clust2))

4.5.1 Seasons

#Create seasons df
seasons_df <- recipes %>% 
  select(ID, rating, all_of(seasons_vec)) %>% 
  filter(if_any(all_of(seasons_vec)) == 1) %>% 
  mutate(sum_season = rowSums(across(all_of(seasons_vec))))

seasons_df %>% 
ggplot(aes(x=sum_season)) +
  geom_bar()

seasons_df %>% 
  filter(sum_season==3)

#>       ID rating winter spring summer fall sum_season
#> 1   1271  4.375      0      1      1    1          3
#> 2   1430  4.375      1      1      0    1          3
#> 3   2672  4.375      0      1      1    1          3
#> 4   2917  3.750      1      1      0    1          3
#> 5   4180  4.375      1      1      0    1          3
#> 6   7740  4.375      1      1      0    1          3
#> 7   8045  4.375      0      1      1    1          3
#> 8  10782  4.375      0      1      1    1          3
#> 9  12421  4.375      1      1      0    1          3
#> 10 12545  5.000      1      1      1    0          3
#> 11 15494  4.375      1      0      1    1          3
#> 12 16603  3.750      1      0      1    1          3
#> 13 16729  4.375      0      1      1    1          3
#> 14 17102  3.750      1      1      0    1          3
#> 15 18603  4.375      1      1      0    1          3
#> 16 18854  5.000      1      1      0    1          3
#> 17 18908  5.000      1      1      1    0          3
#> 18 19913  3.750      1      0      1    1          3

seasons_df %>% 
  filter(sum_season==4)

#>       ID rating winter spring summer fall sum_season
#> 1     54  5.000      1      1      1    1          4
#> 2   1837  3.750      1      1      1    1          4
#> 3   4733  3.125      1      1      1    1          4
#> 4   4980  3.750      1      1      1    1          4
#> 5   7341  1.250      1      1      1    1          4
#> 6   8593  5.000      1      1      1    1          4
#> 7  13759  4.375      1      1      1    1          4
#> 8  14419  3.750      1      1      1    1          4
#> 9  16448  2.500      1      1      1    1          4
#> 10 19011  3.750      1      1      1    1          4

#total of 29 recipes with either 3 or 4 --> let's discard them

#let's look a bit more closely to those with 2 seasons to see if they are next to each other or not

seasons_df %>% 
  filter(sum_season==2)

#>        ID rating winter spring summer fall sum_season
#> 1      25  3.750      1      0      0    1          2
#> 2      27  3.750      0      1      1    0          2
#> 3      57  4.375      1      0      0    1          2
#> 4      67  3.125      1      0      0    1          2
#> 5     130  4.375      1      0      0    1          2
#> 6     135  3.750      1      0      0    1          2
#> 7     153  4.375      0      0      1    1          2
#> 8     156  4.375      1      0      0    1          2
#> 9     167  4.375      1      0      0    1          2
#> 10    179  4.375      1      0      0    1          2
#> 11    238  3.750      0      0      1    1          2
#> 12    256  3.750      1      0      0    1          2
#> 13    275  3.750      0      1      1    0          2
#> 14    278  4.375      0      1      1    0          2
#> 15    368  4.375      1      0      0    1          2
#> 16    369  4.375      0      1      1    0          2
#> 17    401  3.750      1      0      0    1          2
#> 18    449  3.750      0      0      1    1          2
#> 19    501  4.375      0      1      1    0          2
#> 20    516  3.125      1      0      0    1          2
#> 21    538  5.000      1      0      0    1          2
#> 22    543  3.125      0      1      1    0          2
#> 23    558  4.375      1      0      0    1          2
#> 24    597  4.375      1      0      0    1          2
#> 25    599  4.375      1      0      0    1          2
#> 26    614  4.375      1      1      0    0          2
#> 27    618  4.375      1      0      0    1          2
#> 28    639  3.750      1      0      0    1          2
#> 29    664  3.750      1      0      0    1          2
#> 30    666  5.000      1      0      0    1          2
#> 31    682  4.375      1      1      0    0          2
#> 32    697  5.000      0      1      1    0          2
#> 33    700  4.375      0      1      1    0          2
#> 34    738  3.750      1      0      0    1          2
#> 35    802  3.750      0      0      1    1          2
#> 36    830  3.750      1      0      0    1          2
#> 37    864  5.000      1      0      0    1          2
#> 38    866  4.375      1      0      0    1          2
#> 39    890  3.125      1      0      0    1          2
#> 40    900  4.375      1      0      0    1          2
#> 41    902  4.375      1      0      0    1          2
#> 42    903  3.750      1      0      0    1          2
#> 43    917  4.375      1      0      0    1          2
#> 44    920  2.500      0      1      1    0          2
#> 45    956  4.375      1      0      0    1          2
#> 46    964  4.375      0      1      1    0          2
#> 47    986  4.375      1      0      0    1          2
#> 48   1003  4.375      1      1      0    0          2
#> 49   1060  4.375      1      0      0    1          2
#> 50   1106  4.375      0      1      1    0          2
#> 51   1134  4.375      1      0      0    1          2
#> 52   1179  3.125      1      0      0    1          2
#> 53   1207  4.375      0      1      1    0          2
#> 54   1212  4.375      0      1      1    0          2
#> 55   1268  4.375      0      1      1    0          2
#> 56   1275  3.750      0      1      1    0          2
#> 57   1290  4.375      1      0      0    1          2
#> 58   1308  3.125      0      0      1    1          2
#> 59   1345  4.375      0      0      1    1          2
#> 60   1346  3.125      1      0      0    1          2
#> 61   1405  5.000      1      0      0    1          2
#> 62   1406  3.750      1      0      0    1          2
#> 63   1411  4.375      1      0      0    1          2
#> 64   1459  3.750      1      0      0    1          2
#> 65   1507  4.375      0      1      1    0          2
#> 66   1528  4.375      0      1      1    0          2
#> 67   1581  5.000      1      0      0    1          2
#> 68   1602  5.000      1      1      0    0          2
#> 69   1648  3.750      0      1      1    0          2
#> 70   1688  3.125      0      1      0    1          2
#> 71   1732  3.750      0      0      1    1          2
#> 72   1744  3.750      1      0      0    1          2
#> 73   1757  3.750      1      0      0    1          2
#> 74   1794  5.000      1      1      0    0          2
#> 75   1797  3.125      1      0      0    1          2
#> 76   1805  4.375      0      1      1    0          2
#> 77   1806  4.375      1      1      0    0          2
#> 78   1832  3.750      1      0      0    1          2
#> 79   1846  5.000      1      0      0    1          2
#> 80   1895  3.750      1      0      0    1          2
#> 81   1932  4.375      1      0      0    1          2
#> 82   1940  4.375      1      0      0    1          2
#> 83   2020  4.375      0      1      1    0          2
#> 84   2035  5.000      0      1      1    0          2
#> 85   2069  5.000      1      0      0    1          2
#> 86   2088  4.375      0      1      1    0          2
#> 87   2093  4.375      1      0      0    1          2
#> 88   2117  4.375      1      0      0    1          2
#> 89   2137  4.375      1      0      0    1          2
#> 90   2207  4.375      0      0      1    1          2
#> 91   2241  3.750      1      0      0    1          2
#> 92   2243  5.000      1      0      0    1          2
#> 93   2245  4.375      1      0      0    1          2
#> 94   2261  5.000      0      1      1    0          2
#> 95   2269  4.375      1      0      0    1          2
#> 96   2328  4.375      1      0      0    1          2
#> 97   2331  4.375      1      0      0    1          2
#> 98   2380  3.750      1      0      0    1          2
#> 99   2417  3.750      0      1      1    0          2
#> 100  2518  3.750      1      0      0    1          2
#> 101  2610  3.750      0      1      1    0          2
#> 102  2620  4.375      1      0      0    1          2
#> 103  2694  3.125      1      0      0    1          2
#> 104  2717  4.375      1      0      0    1          2
#> 105  2727  5.000      0      0      1    1          2
#> 106  2733  3.750      1      0      0    1          2
#> 107  2746  5.000      0      1      1    0          2
#> 108  2756  3.125      1      0      0    1          2
#> 109  2769  4.375      1      0      0    1          2
#> 110  2778  4.375      0      1      1    0          2
#> 111  2825  5.000      1      0      0    1          2
#> 112  2835  3.750      1      0      0    1          2
#> 113  2838  3.750      0      1      1    0          2
#> 114  2842  3.750      1      0      0    1          2
#> 115  2898  3.750      1      0      0    1          2
#> 116  2938  4.375      0      1      1    0          2
#> 117  2952  4.375      0      1      1    0          2
#> 118  2963  5.000      1      0      0    1          2
#> 119  2965  5.000      1      1      0    0          2
#> 120  2966  3.750      1      0      0    1          2
#> 121  3007  3.125      1      0      0    1          2
#> 122  3010  4.375      0      1      0    1          2
#> 123  3049  4.375      1      0      0    1          2
#> 124  3076  4.375      1      0      0    1          2
#> 125  3149  4.375      0      1      1    0          2
#> 126  3172  3.750      1      0      0    1          2
#> 127  3212  4.375      1      0      0    1          2
#> 128  3219  3.750      0      1      1    0          2
#> 129  3233  3.750      1      0      0    1          2
#> 130  3238  5.000      0      1      1    0          2
#> 131  3257  5.000      1      0      0    1          2
#> 132  3310  4.375      1      0      0    1          2
#> 133  3369  4.375      1      0      0    1          2
#> 134  3402  3.750      1      0      0    1          2
#> 135  3406  4.375      1      0      0    1          2
#> 136  3417  4.375      0      1      1    0          2
#> 137  3468  3.125      0      1      1    0          2
#> 138  3548  4.375      1      0      0    1          2
#> 139  3562  4.375      1      0      0    1          2
#> 140  3569  3.750      1      0      0    1          2
#> 141  3600  5.000      1      0      0    1          2
#> 142  3618  5.000      0      1      1    0          2
#> 143  3663  4.375      1      0      0    1          2
#> 144  3698  4.375      1      0      0    1          2
#> 145  3734  4.375      1      0      1    0          2
#> 146  3773  3.750      1      0      0    1          2
#> 147  3881  4.375      0      1      1    0          2
#> 148  3926  4.375      1      0      0    1          2
#> 149  3945  4.375      1      0      0    1          2
#> 150  3985  4.375      1      0      0    1          2
#> 151  4016  3.750      0      1      1    0          2
#> 152  4025  4.375      1      0      0    1          2
#> 153  4040  4.375      0      1      1    0          2
#> 154  4056  4.375      0      1      1    0          2
#> 155  4064  4.375      1      0      0    1          2
#> 156  4074  4.375      0      1      0    1          2
#> 157  4195  4.375      0      1      1    0          2
#> 158  4204  4.375      1      0      0    1          2
#> 159  4241  5.000      0      1      0    1          2
#> 160  4377  4.375      1      0      0    1          2
#> 161  4414  3.125      1      0      0    1          2
#> 162  4473  4.375      1      0      0    1          2
#> 163  4510  4.375      0      0      1    1          2
#> 164  4539  4.375      1      0      0    1          2
#> 165  4552  4.375      1      0      0    1          2
#> 166  4637  4.375      1      0      0    1          2
#> 167  4682  4.375      1      0      0    1          2
#> 168  4737  4.375      1      0      0    1          2
#> 169  4788  4.375      1      0      0    1          2
#> 170  4839  4.375      1      0      0    1          2
#> 171  4887  3.750      1      0      0    1          2
#> 172  4946  4.375      1      0      0    1          2
#> 173  4966  4.375      0      1      1    0          2
#> 174  5149  5.000      0      1      1    0          2
#> 175  5151  4.375      1      0      0    1          2
#> 176  5216  3.750      1      0      0    1          2
#> 177  5235  4.375      0      1      1    0          2
#> 178  5373  4.375      1      0      0    1          2
#> 179  5407  1.875      1      0      0    1          2
#> 180  5410  5.000      1      0      1    0          2
#> 181  5450  3.750      1      0      0    1          2
#> 182  5451  4.375      0      1      1    0          2
#> 183  5485  4.375      0      1      1    0          2
#> 184  5516  3.750      1      1      0    0          2
#> 185  5537  2.500      0      1      1    0          2
#> 186  5597  4.375      1      0      0    1          2
#> 187  5613  3.125      0      0      1    1          2
#> 188  5622  3.750      1      0      0    1          2
#> 189  5655  4.375      1      0      0    1          2
#> 190  5660  4.375      1      1      0    0          2
#> 191  5689  4.375      1      0      0    1          2
#> 192  5757  4.375      0      0      1    1          2
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#> 572 18327  4.375      1      0      0    1          2
#> 573 18446  4.375      1      0      1    0          2
#> 574 18530  3.750      1      0      0    1          2
#> 575 18602  4.375      0      1      1    0          2
#> 576 18825  4.375      1      0      0    1          2
#> 577 18832  3.750      1      0      0    1          2
#> 578 18837  5.000      1      0      0    1          2
#> 579 18852  4.375      1      0      0    1          2
#> 580 18885  4.375      1      0      0    1          2
#> 581 18982  3.750      0      1      1    0          2
#> 582 19108  3.125      1      0      0    1          2
#> 583 19117  3.125      1      0      0    1          2
#> 584 19205  5.000      1      0      0    1          2
#> 585 19273  4.375      1      0      0    1          2
#> 586 19329  4.375      0      1      1    0          2
#> 587 19338  3.750      1      0      0    1          2
#> 588 19369  4.375      0      1      1    0          2
#> 589 19399  3.750      1      0      0    1          2
#> 590 19493  3.750      0      1      1    0          2
#> 591 19539  4.375      0      1      1    0          2
#> 592 19547  3.750      1      0      0    1          2
#> 593 19563  4.375      1      0      0    1          2
#> 594 19615  4.375      1      0      0    1          2
#> 595 19625  4.375      1      0      0    1          2
#> 596 19627  3.750      1      0      0    1          2
#> 597 19643  3.125      0      0      1    1          2
#> 598 19685  4.375      1      0      0    1          2
#> 599 19792  3.750      0      1      1    0          2
#> 600 19802  4.375      1      0      0    1          2
#> 601 19815  4.375      1      0      0    1          2
#> 602 19881  4.375      0      1      1    0          2
#> 603 19889  4.375      1      0      0    1          2
#> 604 19905  5.000      0      1      1    0          2
#> 605 20041  4.375      1      0      0    1          2
#> 606 20049  4.375      0      1      1    0          2

### should we keep those observations???

As we can see, there is again no correlation between seasons and recipe ratings.

seasons_df %>% 
  select(-sum_season, -ID) %>% 
  plot_correlation()

CAN TRY TO feature engineer rating above 4

Inconclusive result, again.

seasons_df <-seasons_df %>% 
  mutate(rating_above_4 = ifelse(rating > 4, 1, 0), rating_5 = ifelse(rating == 5, 1, 0), rating_1.25 = ifelse(rating == 1.25, 1, 0))

seasons_df %>% 
  select(rating, rating_above_4, all_of(seasons_vec))%>% 
  plot_correlation()

seasons_df %>%
  select(rating_5, rating_1.25, all_of(seasons_vec)) %>%
  plot_correlation()

4.5.2 Recipe Type

#TO ADD WHEN COMPLETED

#taking into account only the "main" recipe types
recipe_types_select <- c("breakfast", "brunch", "dessert", "dinner", "lunch")

type_df <- recipes %>% 
  select(ID, rating, all_of(recipe_types_select)) %>% 
  filter(if_any(all_of(recipe_types_select)) == 1) %>% 
  mutate(sum_type= rowSums(across(all_of(recipe_types_select))))

type_df %>% 
  ggplot(aes(x=sum_type)) +
  geom_bar()

type_df %>% 
  filter(sum_type == 2)

#>        ID rating breakfast brunch dessert dinner lunch sum_type
#> 1      27  3.750         0      0       0      1     1        2
#> 2      50  4.375         1      1       0      0     0        2
#> 3      52  3.750         1      1       0      0     0        2
#> 4      58  4.375         1      1       0      0     0        2
#> 5      67  3.125         0      0       0      1     1        2
#> 6     241  4.375         0      0       0      1     1        2
#> 7     304  3.750         0      0       0      1     1        2
#> 8     361  4.375         0      1       0      0     1        2
#> 9     367  4.375         0      1       0      0     1        2
#> 10    389  4.375         0      0       0      1     1        2
#> 11    429  5.000         1      1       0      0     0        2
#> 12    433  4.375         0      0       0      1     1        2
#> 13    449  3.750         0      0       0      1     1        2
#> 14    467  4.375         1      1       0      0     0        2
#> 15    476  4.375         0      1       1      0     0        2
#> 16    501  4.375         0      1       1      0     0        2
#> 17    502  3.750         0      0       0      1     1        2
#> 18    513  5.000         0      0       0      1     1        2
#> 19    514  5.000         0      0       0      1     1        2
#> 20    533  4.375         1      1       0      0     0        2
#> 21    534  3.750         1      1       0      0     0        2
#> 22    558  4.375         0      0       0      1     1        2
#> 23    574  4.375         1      0       1      0     0        2
#> 24    593  3.125         1      1       0      0     0        2
#> 25    610  4.375         0      0       0      1     1        2
#> 26    645  4.375         1      1       0      0     0        2
#> 27    648  3.750         1      1       0      0     0        2
#> 28    694  4.375         1      1       0      0     0        2
#> 29    728  3.125         1      1       0      0     0        2
#> 30    730  4.375         0      0       0      1     1        2
#> 31    813  3.750         0      1       1      0     0        2
#> 32    890  3.125         0      0       0      1     1        2
#> 33    909  3.750         1      1       0      0     0        2
#> 34    924  3.750         0      0       0      1     1        2
#> 35   1020  3.125         0      0       0      1     1        2
#> 36   1035  4.375         0      1       0      0     1        2
#> 37   1038  3.125         0      0       0      1     1        2
#> 38   1057  3.750         0      0       0      1     1        2
#> 39   1129  4.375         0      0       0      1     1        2
#> 40   1179  3.125         1      0       1      0     0        2
#> 41   1185  4.375         1      1       0      0     0        2
#> 42   1207  4.375         0      0       0      1     1        2
#> 43   1245  4.375         0      0       0      1     1        2
#> 44   1262  4.375         0      0       0      1     1        2
#> 45   1285  4.375         0      0       0      1     1        2
#> 46   1315  3.125         0      0       0      1     1        2
#> 47   1329  3.750         1      1       0      0     0        2
#> 48   1346  3.125         1      1       0      0     0        2
#> 49   1387  3.750         0      0       0      1     1        2
#> 50   1426  4.375         1      1       0      0     0        2
#> 51   1453  3.750         1      1       0      0     0        2
#> 52   1456  5.000         1      1       0      0     0        2
#> 53   1511  4.375         0      0       0      1     1        2
#> 54   1525  3.750         0      0       0      1     1        2
#> 55   1528  4.375         0      0       0      1     1        2
#> 56   1537  5.000         0      0       0      1     1        2
#> 57   1540  3.750         0      0       0      1     1        2
#> 58   1613  4.375         0      0       0      1     1        2
#> 59   1625  4.375         0      1       1      0     0        2
#> 60   1648  3.750         0      0       0      1     1        2
#> 61   1659  5.000         1      1       0      0     0        2
#> 62   1725  4.375         1      1       0      0     0        2
#> 63   1782  4.375         0      0       0      1     1        2
#> 64   1803  4.375         0      0       0      1     1        2
#> 65   1829  3.125         0      0       0      1     1        2
#> 66   1874  5.000         0      0       0      1     1        2
#> 67   1933  3.750         1      1       0      0     0        2
#> 68   1940  4.375         0      0       0      1     1        2
#> 69   1965  3.750         1      1       0      0     0        2
#> 70   2035  5.000         0      0       0      1     1        2
#> 71   2041  3.750         1      1       0      0     0        2
#> 72   2092  4.375         0      0       0      1     1        2
#> 73   2139  3.750         1      1       0      0     0        2
#> 74   2184  1.875         1      1       0      0     0        2
#> 75   2413  4.375         1      1       0      0     0        2
#> 76   2421  4.375         0      0       0      1     1        2
#> 77   2478  3.125         0      0       0      1     1        2
#> 78   2489  3.750         1      1       0      0     0        2
#> 79   2518  3.750         0      0       0      1     1        2
#> 80   2528  2.500         1      1       0      0     0        2
#> 81   2540  2.500         1      1       0      0     0        2
#> 82   2604  4.375         1      1       0      0     0        2
#> 83   2634  4.375         1      1       0      0     0        2
#> 84   2710  3.750         1      1       0      0     0        2
#> 85   2738  3.750         0      0       0      1     1        2
#> 86   2745  3.125         0      1       1      0     0        2
#> 87   2769  4.375         0      0       0      1     1        2
#> 88   2799  4.375         1      1       0      0     0        2
#> 89   2812  4.375         1      1       0      0     0        2
#> 90   2847  4.375         0      1       0      0     1        2
#> 91   2865  4.375         1      1       0      0     0        2
#> 92   2884  4.375         0      0       0      1     1        2
#> 93   2895  4.375         1      1       0      0     0        2
#> 94   2904  5.000         0      0       0      1     1        2
#> 95   2912  3.125         1      1       0      0     0        2
#> 96   3001  4.375         1      1       0      0     0        2
#> 97   3025  4.375         0      0       0      1     1        2
#> 98   3097  5.000         1      1       0      0     0        2
#> 99   3157  4.375         0      1       1      0     0        2
#> 100  3172  3.750         0      0       0      1     1        2
#> 101  3234  4.375         0      0       0      1     1        2
#> 102  3245  4.375         1      1       0      0     0        2
#> 103  3342  4.375         0      1       0      0     1        2
#> 104  3354  4.375         1      1       0      0     0        2
#> 105  3405  4.375         0      0       0      1     1        2
#> 106  3423  4.375         1      1       0      0     0        2
#> 107  3492  4.375         0      0       0      1     1        2
#> 108  3524  5.000         0      0       0      1     1        2
#> 109  3572  5.000         0      0       0      1     1        2
#> 110  3670  4.375         1      1       0      0     0        2
#> 111  3734  4.375         0      0       0      1     1        2
#> 112  3806  4.375         0      0       0      1     1        2
#> 113  3811  3.750         1      1       0      0     0        2
#> 114  3855  3.750         1      1       0      0     0        2
#> 115  3926  4.375         1      1       0      0     0        2
#> 116  3983  4.375         1      1       0      0     0        2
#> 117  3985  4.375         0      0       0      1     1        2
#> 118  3997  3.750         0      0       0      1     1        2
#> 119  4014  4.375         0      0       0      1     1        2
#> 120  4038  4.375         1      1       0      0     0        2
#> 121  4092  4.375         1      1       0      0     0        2
#> 122  4096  4.375         0      0       0      1     1        2
#> 123  4097  3.750         0      0       0      1     1        2
#> 124  4101  1.250         1      0       1      0     0        2
#> 125  4122  4.375         0      1       0      0     1        2
#> 126  4135  5.000         1      1       0      0     0        2
#> 127  4204  4.375         0      0       0      1     1        2
#> 128  4207  3.750         0      0       0      1     1        2
#> 129  4227  5.000         1      1       0      0     0        2
#> 130  4236  4.375         0      0       0      1     1        2
#> 131  4274  4.375         1      1       0      0     0        2
#> 132  4289  2.500         1      0       1      0     0        2
#> 133  4337  3.750         1      1       0      0     0        2
#> 134  4489  2.500         1      1       0      0     0        2
#> 135  4498  4.375         1      1       0      0     0        2
#> 136  4537  4.375         1      1       0      0     0        2
#> 137  4547  3.750         1      1       0      0     0        2
#> 138  4559  2.500         1      1       0      0     0        2
#> 139  4668  3.125         0      1       1      0     0        2
#> 140  4682  4.375         0      0       0      1     1        2
#> 141  4744  2.500         1      1       0      0     0        2
#> 142  4775  4.375         1      1       0      0     0        2
#> 143  4779  3.125         1      1       0      0     0        2
#> 144  4788  4.375         1      1       0      0     0        2
#> 145  4796  5.000         0      1       1      0     0        2
#> 146  4803  3.750         0      0       0      1     1        2
#> 147  4825  5.000         0      0       0      1     1        2
#> 148  4833  3.750         0      0       0      1     1        2
#> 149  4834  3.750         0      0       0      1     1        2
#> 150  4854  4.375         0      0       0      1     1        2
#> 151  4902  3.750         0      1       1      0     0        2
#> 152  4903  5.000         1      1       0      0     0        2
#> 153  4919  3.125         0      0       0      1     1        2
#> 154  4942  4.375         1      1       0      0     0        2
#> 155  4980  3.750         1      1       0      0     0        2
#> 156  4987  3.750         0      0       0      1     1        2
#> 157  5048  5.000         0      0       0      1     1        2
#> 158  5057  1.875         0      0       0      1     1        2
#> 159  5207  4.375         1      1       0      0     0        2
#> 160  5224  4.375         0      0       0      1     1        2
#> 161  5238  4.375         0      0       0      1     1        2
#> 162  5304  4.375         0      0       0      1     1        2
#> 163  5314  5.000         1      1       0      0     0        2
#> 164  5332  4.375         0      0       0      1     1        2
#> 165  5355  3.750         1      1       0      0     0        2
#> 166  5413  4.375         0      1       1      0     0        2
#> 167  5448  4.375         1      1       0      0     0        2
#> 168  5505  3.750         1      1       0      0     0        2
#> 169  5524  4.375         1      1       0      0     0        2
#> 170  5537  2.500         0      0       0      1     1        2
#> 171  5581  4.375         1      1       0      0     0        2
#> 172  5642  5.000         0      0       0      1     1        2
#> 173  5694  3.750         1      1       0      0     0        2
#> 174  5713  3.750         1      1       0      0     0        2
#> 175  5818  3.750         1      1       0      0     0        2
#> 176  5833  3.125         1      1       0      0     0        2
#> 177  5876  4.375         0      0       0      1     1        2
#> 178  5880  5.000         0      1       0      0     1        2
#> 179  5969  4.375         0      1       0      1     0        2
#> 180  6077  2.500         0      0       0      1     1        2
#> 181  6080  3.750         1      0       1      0     0        2
#> 182  6164  3.750         0      1       1      0     0        2
#> 183  6169  3.750         1      1       0      0     0        2
#> 184  6234  5.000         0      1       1      0     0        2
#> 185  6269  4.375         0      0       0      1     1        2
#> 186  6308  5.000         0      1       0      0     1        2
#> 187  6373  3.125         0      0       0      1     1        2
#> 188  6386  4.375         0      1       1      0     0        2
#> 189  6407  3.750         0      0       0      1     1        2
#> 190  6438  4.375         0      0       0      1     1        2
#> 191  6466  4.375         1      1       0      0     0        2
#> 192  6487  4.375         0      0       0      1     1        2
#> 193  6529  3.750         1      1       0      0     0        2
#> 194  6550  4.375         0      1       0      0     1        2
#> 195  6578  3.750         0      0       0      1     1        2
#> 196  6621  4.375         0      0       0      1     1        2
#> 197  6759  4.375         0      0       0      1     1        2
#> 198  6769  4.375         0      0       0      1     1        2
#> 199  6780  4.375         0      0       0      1     1        2
#> 200  6816  3.750         1      1       0      0     0        2
#> 201  6830  4.375         1      1       0      0     0        2
#> 202  6839  4.375         0      0       0      1     1        2
#> 203  6847  4.375         0      0       0      1     1        2
#> 204  6857  4.375         1      1       0      0     0        2
#> 205  6884  3.750         0      0       0      1     1        2
#> 206  6902  4.375         1      1       0      0     0        2
#> 207  6904  5.000         1      1       0      0     0        2
#> 208  6927  5.000         0      0       0      1     1        2
#> 209  6931  3.750         0      0       0      1     1        2
#> 210  6960  5.000         0      0       0      1     1        2
#> 211  7009  4.375         0      0       0      1     1        2
#> 212  7010  5.000         0      0       0      1     1        2
#> 213  7028  4.375         1      1       0      0     0        2
#> 214  7087  4.375         1      1       0      0     0        2
#> 215  7126  4.375         0      1       0      0     1        2
#> 216  7264  4.375         1      0       1      0     0        2
#> 217  7300  4.375         0      0       0      1     1        2
#> 218  7308  4.375         1      1       0      0     0        2
#> 219  7353  3.750         0      0       0      1     1        2
#> 220  7364  4.375         0      0       0      1     1        2
#> 221  7369  4.375         0      0       1      1     0        2
#> 222  7404  4.375         0      1       1      0     0        2
#> 223  7444  3.750         1      1       0      0     0        2
#> 224  7463  3.750         1      1       0      0     0        2
#> 225  7483  5.000         1      1       0      0     0        2
#> 226  7525  4.375         1      1       0      0     0        2
#> 227  7565  3.750         0      0       0      1     1        2
#> 228  7586  4.375         1      0       0      0     1        2
#> 229  7612  5.000         0      0       0      1     1        2
#> 230  7708  5.000         1      1       0      0     0        2
#> 231  7743  5.000         0      0       0      1     1        2
#> 232  7783  4.375         0      0       0      1     1        2
#> 233  7836  4.375         1      1       0      0     0        2
#> 234  7858  4.375         1      1       0      0     0        2
#> 235  7869  3.750         1      1       0      0     0        2
#> 236  7983  4.375         0      1       1      0     0        2
#> 237  8093  4.375         0      0       0      1     1        2
#> 238  8102  4.375         0      1       1      0     0        2
#> 239  8135  5.000         1      1       0      0     0        2
#> 240  8146  3.750         0      0       0      1     1        2
#> 241  8157  4.375         1      1       0      0     0        2
#> 242  8158  4.375         0      1       1      0     0        2
#> 243  8227  5.000         1      1       0      0     0        2
#> 244  8253  4.375         0      0       0      1     1        2
#> 245  8270  4.375         0      0       0      1     1        2
#> 246  8314  3.750         0      0       0      1     1        2
#> 247  8321  4.375         0      0       0      1     1        2
#> 248  8372  5.000         1      1       0      0     0        2
#> 249  8429  3.125         0      0       0      1     1        2
#> 250  8459  3.750         0      0       0      1     1        2
#> 251  8476  4.375         1      1       0      0     0        2
#> 252  8521  4.375         0      0       0      1     1        2
#> 253  8524  3.750         0      0       0      1     1        2
#> 254  8609  5.000         0      0       0      1     1        2
#> 255  8628  3.750         1      1       0      0     0        2
#> 256  8641  3.750         0      0       0      1     1        2
#> 257  8677  4.375         1      1       0      0     0        2
#> 258  8738  4.375         1      1       0      0     0        2
#> 259  8745  3.125         1      1       0      0     0        2
#> 260  8747  4.375         0      0       0      1     1        2
#> 261  8755  4.375         0      0       0      1     1        2
#> 262  8759  4.375         1      1       0      0     0        2
#> 263  8778  4.375         0      0       0      1     1        2
#> 264  8818  3.750         1      1       0      0     0        2
#> 265  8820  5.000         0      0       0      1     1        2
#> 266  8821  3.750         1      1       0      0     0        2
#> 267  8830  4.375         0      0       0      1     1        2
#> 268  8883  4.375         0      0       0      1     1        2
#> 269  8916  4.375         0      1       1      0     0        2
#> 270  8923  4.375         1      1       0      0     0        2
#> 271  8936  3.750         0      0       0      1     1        2
#> 272  8954  4.375         1      0       1      0     0        2
#> 273  8987  4.375         0      0       0      1     1        2
#> 274  8989  4.375         0      0       0      1     1        2
#> 275  8999  2.500         0      0       0      1     1        2
#> 276  9046  5.000         0      0       0      1     1        2
#> 277  9066  5.000         0      0       1      1     0        2
#> 278  9070  3.750         0      1       0      0     1        2
#> 279  9122  4.375         1      1       0      0     0        2
#> 280  9141  4.375         1      1       0      0     0        2
#> 281  9169  5.000         0      0       0      1     1        2
#> 282  9173  4.375         1      1       0      0     0        2
#> 283  9269  4.375         0      1       1      0     0        2
#> 284  9358  5.000         1      0       1      0     0        2
#> 285  9365  3.750         0      0       0      1     1        2
#> 286  9367  4.375         1      1       0      0     0        2
#> 287  9396  3.750         0      0       0      1     1        2
#> 288  9405  3.750         1      1       0      0     0        2
#> 289  9420  4.375         0      1       1      0     0        2
#> 290  9428  4.375         1      1       0      0     0        2
#> 291  9554  4.375         0      1       0      1     0        2
#> 292  9557  3.750         1      1       0      0     0        2
#> 293  9598  4.375         0      0       0      1     1        2
#> 294  9628  5.000         0      0       0      1     1        2
#> 295  9643  2.500         0      1       0      0     1        2
#> 296  9699  4.375         0      0       0      1     1        2
#> 297  9816  3.125         1      1       0      0     0        2
#> 298  9844  4.375         1      1       0      0     0        2
#> 299  9917  4.375         0      0       0      1     1        2
#> 300  9955  3.750         0      1       1      0     0        2
#> 301  9957  5.000         1      1       0      0     0        2
#> 302  9961  4.375         0      0       0      1     1        2
#> 303 10009  3.125         0      0       0      1     1        2
#> 304 10019  3.750         0      0       0      1     1        2
#> 305 10067  3.750         0      0       0      1     1        2
#> 306 10088  4.375         0      1       1      0     0        2
#> 307 10105  4.375         1      1       0      0     0        2
#> 308 10152  4.375         1      1       0      0     0        2
#> 309 10239  3.750         0      0       0      1     1        2
#> 310 10363  2.500         1      1       0      0     0        2
#> 311 10373  3.750         1      1       0      0     0        2
#> 312 10393  3.125         0      0       0      1     1        2
#> 313 10430  3.750         1      1       0      0     0        2
#> 314 10564  4.375         0      0       0      1     1        2
#> 315 10598  5.000         1      1       0      0     0        2
#> 316 10690  5.000         1      1       0      0     0        2
#> 317 10748  4.375         0      1       1      0     0        2
#> 318 10763  2.500         0      0       0      1     1        2
#> 319 10770  4.375         1      1       0      0     0        2
#> 320 10800  3.750         1      1       0      0     0        2
#> 321 10907  4.375         1      1       0      0     0        2
#> 322 10926  4.375         1      1       0      0     0        2
#> 323 10928  5.000         0      0       0      1     1        2
#> 324 10959  4.375         1      1       0      0     0        2
#> 325 10967  3.750         1      1       0      0     0        2
#> 326 11010  5.000         1      0       1      0     0        2
#> 327 11046  4.375         0      0       0      1     1        2
#> 328 11048  3.750         0      1       0      0     1        2
#> 329 11097  5.000         0      1       1      0     0        2
#> 330 11101  1.250         0      0       0      1     1        2
#> 331 11124  4.375         1      1       0      0     0        2
#> 332 11135  2.500         0      1       1      0     0        2
#> 333 11157  5.000         0      0       0      1     1        2
#> 334 11169  5.000         0      0       0      1     1        2
#> 335 11177  3.750         0      0       0      1     1        2
#> 336 11215  3.750         1      1       0      0     0        2
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#> 341 11315  3.750         0      1       1      0     0        2
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#> 347 11660  5.000         0      0       0      1     1        2
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#> 357 11847  3.750         1      1       0      0     0        2
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#> 365 12202  5.000         1      1       0      0     0        2
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#> 367 12239  3.750         0      0       0      1     1        2
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#> 371 12469  3.125         1      0       1      0     0        2
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#> 373 12539  3.750         1      1       0      0     0        2
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#> 376 12580  3.750         1      1       0      0     0        2
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#> 381 12773  3.750         1      1       0      0     0        2
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#> 387 12984  3.750         0      0       0      1     1        2
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#> 397 13273  5.000         1      1       0      0     0        2
#> 398 13284  3.750         0      0       0      1     1        2
#> 399 13287  1.875         1      1       0      0     0        2
#> 400 13304  4.375         0      1       0      0     1        2
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#> 402 13339  3.750         0      0       0      1     1        2
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#> 404 13377  3.750         0      0       0      1     1        2
#> 405 13412  4.375         0      0       0      1     1        2
#> 406 13417  2.500         1      1       0      0     0        2
#> 407 13428  3.750         1      1       0      0     0        2
#> 408 13458  3.750         0      0       0      1     1        2
#> 409 13553  5.000         1      1       0      0     0        2
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#> 414 13916  3.750         0      0       0      1     1        2
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#> 419 14095  3.750         1      1       0      0     0        2
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#> 422 14210  2.500         0      0       0      1     1        2
#> 423 14213  4.375         1      0       1      0     0        2
#> 424 14219  3.125         0      0       0      1     1        2
#> 425 14284  4.375         0      0       0      1     1        2
#> 426 14298  3.125         0      1       0      0     1        2
#> 427 14332  5.000         1      1       0      0     0        2
#> 428 14336  3.750         0      0       0      1     1        2
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#> 431 14506  5.000         1      1       0      0     0        2
#> 432 14536  3.750         1      1       0      0     0        2
#> 433 14581  5.000         0      0       0      1     1        2
#> 434 14639  3.750         1      1       0      0     0        2
#> 435 14696  5.000         1      1       0      0     0        2
#> 436 14729  3.750         0      0       0      1     1        2
#> 437 14739  3.750         1      1       0      0     0        2
#> 438 14774  4.375         0      1       1      0     0        2
#> 439 14832  3.750         1      0       1      0     0        2
#> 440 14845  3.125         1      1       0      0     0        2
#> 441 14950  3.750         1      1       0      0     0        2
#> 442 15014  3.750         0      1       0      0     1        2
#> 443 15053  3.750         0      0       0      1     1        2
#> 444 15078  5.000         1      0       1      0     0        2
#> 445 15115  2.500         0      0       0      1     1        2
#> 446 15185  3.125         1      1       0      0     0        2
#> 447 15188  4.375         1      1       0      0     0        2
#> 448 15194  4.375         1      1       0      0     0        2
#> 449 15206  4.375         1      1       0      0     0        2
#> 450 15266  4.375         1      1       0      0     0        2
#> 451 15276  5.000         0      0       0      1     1        2
#> 452 15289  3.125         0      1       1      0     0        2
#> 453 15302  4.375         0      0       0      1     1        2
#> 454 15303  4.375         0      0       0      1     1        2
#> 455 15307  4.375         0      0       0      1     1        2
#> 456 15309  5.000         0      1       1      0     0        2
#> 457 15388  5.000         1      1       0      0     0        2
#> 458 15397  4.375         1      1       0      0     0        2
#> 459 15410  4.375         0      0       0      1     1        2
#> 460 15430  4.375         1      1       0      0     0        2
#> 461 15518  4.375         1      1       0      0     0        2
#> 462 15683  3.750         0      1       1      0     0        2
#> 463 15703  3.125         1      1       0      0     0        2
#> 464 15707  5.000         0      0       0      1     1        2
#> 465 15738  4.375         1      1       0      0     0        2
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#> 467 15768  5.000         0      1       1      0     0        2
#> 468 15793  3.750         0      0       0      1     1        2
#> 469 15836  3.750         0      0       0      1     1        2
#> 470 15886  4.375         1      1       0      0     0        2
#> 471 15915  1.250         0      0       0      1     1        2
#> 472 15943  3.750         0      0       0      1     1        2
#> 473 15945  3.750         1      1       0      0     0        2
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#> 478 16322  3.750         0      0       0      1     1        2
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#> 482 16622  3.750         0      0       0      1     1        2
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#> 491 16903  3.750         0      0       0      1     1        2
#> 492 16908  5.000         0      0       0      1     1        2
#> 493 16941  3.750         0      1       0      0     1        2
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#> 495 17007  4.375         1      1       0      0     0        2
#> 496 17031  3.125         1      1       0      0     0        2
#> 497 17067  3.750         1      1       0      0     0        2
#> 498 17105  5.000         0      0       1      0     1        2
#> 499 17137  4.375         0      0       0      1     1        2
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#> 501 17215  5.000         1      1       0      0     0        2
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#> 504 17397  4.375         1      1       0      0     0        2
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#> 506 17495  3.750         0      0       0      1     1        2
#> 507 17507  3.750         1      1       0      0     0        2
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#> 509 17555  3.750         1      0       1      0     0        2
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#> 511 17600  5.000         1      1       0      0     0        2
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#> 513 17666  3.750         1      1       0      0     0        2
#> 514 17715  3.750         0      0       0      1     1        2
#> 515 17742  3.125         1      0       1      0     0        2
#> 516 17764  3.750         0      0       0      1     1        2
#> 517 17793  3.750         1      1       0      0     0        2
#> 518 17814  3.125         1      1       0      0     0        2
#> 519 17823  3.750         1      1       0      0     0        2
#> 520 17833  5.000         1      1       0      0     0        2
#> 521 17913  4.375         0      0       0      1     1        2
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#> 523 18078  3.125         0      1       0      0     1        2
#> 524 18116  4.375         0      0       0      1     1        2
#> 525 18121  3.750         0      0       0      1     1        2
#> 526 18140  3.750         1      1       0      0     0        2
#> 527 18141  1.250         1      1       0      0     0        2
#> 528 18173  3.750         1      1       0      0     0        2
#> 529 18179  4.375         0      0       0      1     1        2
#> 530 18238  3.750         1      1       0      0     0        2
#> 531 18242  4.375         0      0       0      1     1        2
#> 532 18272  3.750         1      1       0      0     0        2
#> 533 18349  4.375         0      1       1      0     0        2
#> 534 18357  3.750         1      1       0      0     0        2
#> 535 18408  2.500         0      0       0      1     1        2
#> 536 18440  4.375         0      1       0      0     1        2
#> 537 18464  4.375         0      0       0      1     1        2
#> 538 18548  4.375         0      0       0      1     1        2
#> 539 18579  3.750         1      1       0      0     0        2
#> 540 18587  3.750         1      1       0      0     0        2
#> 541 18595  4.375         0      0       0      1     1        2
#> 542 18612  4.375         0      0       0      1     1        2
#> 543 18706  5.000         1      1       0      0     0        2
#> 544 18756  5.000         0      0       0      1     1        2
#> 545 18786  3.750         1      0       1      0     0        2
#> 546 18792  4.375         1      1       0      0     0        2
#> 547 18885  4.375         0      0       0      1     1        2
#> 548 18898  4.375         0      0       0      1     1        2
#> 549 18948  4.375         0      0       0      1     1        2
#> 550 18959  3.750         1      1       0      0     0        2
#> 551 18983  4.375         0      0       0      1     1        2
#> 552 19029  2.500         1      1       0      0     0        2
#> 553 19046  3.750         0      0       0      1     1        2
#> 554 19118  4.375         0      1       1      0     0        2
#> 555 19160  5.000         0      1       1      0     0        2
#> 556 19208  4.375         0      0       0      1     1        2
#> 557 19278  4.375         0      0       0      1     1        2
#> 558 19310  3.750         0      0       0      1     1        2
#> 559 19345  4.375         0      0       0      1     1        2
#> 560 19357  3.750         1      1       0      0     0        2
#> 561 19371  3.125         0      1       1      0     0        2
#> 562 19400  5.000         1      1       0      0     0        2
#> 563 19423  3.750         0      0       0      1     1        2
#> 564 19429  3.125         1      1       0      0     0        2
#> 565 19469  4.375         1      1       0      0     0        2
#> 566 19470  4.375         0      0       0      1     1        2
#> 567 19472  3.125         0      0       0      1     1        2
#> 568 19547  3.750         0      0       0      1     1        2
#> 569 19586  4.375         0      0       0      1     1        2
#> 570 19623  4.375         1      1       0      0     0        2
#> 571 19633  3.125         1      1       0      0     0        2
#> 572 19650  3.750         0      0       0      1     1        2
#> 573 19698  4.375         1      1       0      0     0        2
#> 574 19726  3.750         1      1       0      0     0        2
#> 575 19760  3.125         0      1       0      0     1        2
#> 576 19812  1.250         0      0       0      1     1        2
#> 577 19881  4.375         0      0       0      1     1        2
#> 578 19906  3.750         0      1       0      0     1        2
#> 579 19941  3.750         1      0       1      0     0        2
#> 580 19967  4.375         1      1       0      0     0        2
#> 581 20018  4.375         1      1       0      0     0        2

# left with 3333 obs after filtering
type_df <- type_df %>% 
  filter(!sum_type >1)

Once again very low correlation for recipe types, we will not included them in the analysis.

type_df %>% 
  select(-c(ID, sum_type)) %>% 
  plot_correlation()

### Countries Only 54 recipes containing info about the country so we can’t use it either for the analysis.

countries_df <- recipes %>% 
  select(ID, rating, all_of(countries)) %>% 
  filter(if_any(all_of(countries)) == 1) %>% 
  mutate(sum_type= rowSums(across(all_of(countries))))

countries_df %>% 
  ggplot(aes(x=sum_type)) +
  geom_bar()

#,'03_supervised_learning.Rmd', '04_unsupervised_learning.Rmd'

Questions - should we convert the binary columns to factor or can we leave them as integer for modelling? - should we balance the data, in the rating_bin case and in the rating normal with 7 classes - should we normalise the numerical data - it’s mentionned in the slides that the validation set should not be balanced, but how do we do that using train() with caret? - should we really used balanced data for training? Because at least for KNN it always makes K=1 better, whereas K is was larger when we trained with unbalanced data Apparently for KNN, it’s not required to balance data